DEA-C01 Exam Dumps and Practice Questions

For high-performance analytics in Athena and EMR Spark, the columnar storage formats Parquet and ORC deliver the shortest query times. These formats minimize I/O, enable predicate pushdown, and support efficient compression and parallel reads.

When integrated with the AWS Glue Data Catalog, schema evolution is automatically tracked and managed across queries.

This combination (Parquet/ORC + Glue Catalog) is explicitly recommended by AWS for analytical workloads on S3 data lakes.

''For analytical workloads using Amazon EMR and Athena, store data in columnar formats such as Parquet or ORC. These formats provide optimized compression and query performance.''

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''Use the AWS Glue Data Catalog to manage table schemas and schema evolution for columnar data formats such as Parquet and ORC.''

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Therefore, using ORC or Parquet along with the Glue Data Catalog achieves the shortest Athena query times and seamless schema management.

To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is 'usa'. The correct SQL syntax would be:

Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa';This statement inserts only the cities and states where the country column has a value of 'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.

Options B, C, and D are incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).

Amazon Athena SQL Reference

Creating Tables in Athena

The DatasetMatch rule in DQDL checks for full value equivalence between mapped fields. A value of 1.0 indicates a 100% match. The correct syntax and metric for an exact match scenario are:

''Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match.''

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Options with ''100'' use incorrect syntax since DQDL uses floating-point scores (e.g., 1.0, 0.95), not percentages.

Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.

Amazon Athena Federated Queries:

Athena's federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena's support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.

The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.

Alternatives Considered:

A (AWS Glue pipeline): This would work but requires more operational effort to manage and transform the data in AWS Glue.

C (Amazon EMR): Using EMR and writing PySpark code introduces more operational overhead and complexity compared to a SQL-based solution in Athena.

D (Amazon AppFlow): AppFlow is more suitable for transferring data between services but is not as efficient for transformations and joins as Athena federated queries.

Amazon Athena Documentation

Federated Queries in Amazon Athena

S3 Intelligent-Tiering is a storage class that automatically moves objects between four access tiers based on the changing access patterns. The default access tier consists of two tiers: Frequent Access and Infrequent Access. Objects in the Frequent Access tier have the same performance and availability as S3 Standard, while objects in the Infrequent Access tier have the same performance and availability as S3 Standard-IA. S3 Intelligent-Tiering monitors the access patterns of each object and moves them between the tiers accordingly, without any operational overhead or retrieval fees. This solution can optimize S3 storage costs for data with unpredictable and variable access patterns, while ensuring millisecond latency for data retrieval. The other solutions are not optimal or relevant for this requirement. Using S3 Storage Lens standard metrics and activity metrics can provide insights into the storage usage and access patterns, but they do not automate the data movement between storage classes. Creating S3 Lifecycle policies for the S3 buckets can move objects to more cost-optimized storage classes, but they require manual configuration and maintenance, and they may incur retrieval fees for data that is accessed unexpectedly. Activating the Deep Archive Access tier for S3 Intelligent-Tiering can further reduce the storage costs for data that is rarely accessed, but it also increases the retrieval time to 12 hours, which does not meet the requirement of millisecond latency.Reference:

S3 Intelligent-Tiering

S3 Storage Lens

S3 Lifecycle policies

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